Modelling variation and temporal dynamics of individual tree defoliation caused by spruce budworm in Maine, US and New Brunswick, Canada
Bibliographic record
Abstract
Insect defoliation reduces the growth and survival of trees. Evaluating these effects on trees requires understandings of the variation and dynamics of defoliation, which has been limited by the coarseness of analytical scales. This is especially the case for defoliation caused by spruce budworm (SBW; Choristoneura fumiferana (Clem.)), the primary forest defoliator in North America. In this study, we developed Bayesian models based on a Markov chain Monte Carlo technique to evaluate patterns of SBW defoliation by predicting individual tree defoliation using stand-level measurements of defoliation that are potentially more efficient to obtain through remote sensing. Additionally, the temporal development of individual tree defoliation was also analysed by the same modelling approach. Data containing over 47 000 observations of individual tree defoliation collected during the last SBW outbreak in the 1970s–1980s from an extensive network of permanent sample plots in Maine, US and New Brunswick, Canada were used in model development. Our results demonstrated that the variation in individual tree defoliation was predominantly dependent on species, while all the other examined tree-, stand- and site-level characteristics had a more limited influence individually or even in combination. Despite significant species-specific differences in magnitude, defoliation of both balsam fir (Abies balsamea L.) and red/black spruce (Picea rubens Sarg., Picea mariana (Mill.) B.S.P.) developed towards their respective converged trajectories regardless of differences in initial defoliation, and other examined tree-, stand- and site-level characteristics. These findings were consistent between Maine and New Brunswick despite varying forest management history and species composition. Overall, the results highlight the high variability in SBW defoliation, while the developed modelling framework should be extendable to other regions and other forms of defoliation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".